Ant Group's in-house AI lab is worth watching if you use or study open-weight models: InclusionAI ships free, MIT-licensed trillion-parameter models like Ling-2.6-1T and Ring-2.6-1T from its Hangzhou base, competing directly with DeepSeek and Alibaba's Qwen team on frontier open research.
InclusionAI is Ant Group's open AGI research lab, founded in March 2025. It builds four open-weight model lines under MIT license: Ling for efficient general use, Ring for agentic reasoning, Ming for multimodal generation, and LLaDA for diffusion-based text. Flagship models scale past 1T parameters, trained partly on domestic Chinese chips instead of Nvidia GPUs.
Founded: 2025 · HQ: Hangzhou, Zhejiang, China · Team: 50-200 · Funding: Internally funded as an Ant Group research division; no separate funding rounds, external investors, or valuation disclosed
About InclusionAI
InclusionAI was established in March 2025 as an open AGI research initiative inside Ant Group, the Hangzhou-based fintech company spun out of the Alibaba family that also owns Alipay. The lab is not separately incorporated; it operates as an internal Ant Group division and is led at the executive level by Zhengyu He, Ant Group's chief technology officer and a former academic researcher known for early work on GPU-accelerated maximum-flow algorithms. Richard Bian, who previously ran the AntOSS open-source program at Ant Group, leads product and growth for InclusionAI and its Ant Ling model line. The team is staffed with engineers and researchers who previously worked at OpenAI, Google and Meta's FAIR lab, alongside Ant Group's existing machine learning organization. InclusionAI's output splits into four open-weight model lines. Ling is the token-efficient, non-reasoning foundation line: it started with Ling-1T, Ant's first trillion-parameter language model, unveiled in October 2025, and has since progressed through Ling-2.5-1T and Ling-2.6-1T, its current flagship. Ring is the explicit-reasoning counterpart, derived from the same Ling backbone and tuned for agentic workflows such as coding agents, tool use and long-horizon multi-step execution; Ring-2.6-1T is a trillion-parameter mixture-of-experts model with roughly 63 billion active parameters per token. Ming is InclusionAI's multimodal, any-to-any line built on top of Ling, culminating in Ming-Flash-Omni-2.0, which the lab describes as the first open model to unify speech, audio and music generation within one architecture. LLaDA is a separate diffusion-based language model line, most recently updated as LLaDA 2.1 with token-editing acceleration. Every model line is released under the MIT license, with weights distributed through Hugging Face, ModelScope and GitHub rather than sold as a hosted API. The most recent release cycle has been dense. Ming-Flash-Omni-2.0 shipped in February 2026, the same month InclusionAI published Ling-2.5-1T and Ring-2.5-1T alongside real-world benchmark data, including an IMO 2025 gold-medal-standard score of 35 out of 42 for Ring-2.5-1T and a China Mathematical Olympiad score that beat the national team's qualifying cutoff. Ling-2.6-1T followed in April 2026 as the new head of the Ling line, and by September 2026 the lineup had grown further downmarket with Ling-3.0-tiny, a 7.9-billion-parameter model InclusionAI positions on the intelligence-per-active-parameter frontier, and Ling-3.0-flash-Fin, a finance-domain fine-tune of Ling-3.0-flash built with financial institutions and domain experts for investment research and spreadsheet-heavy workflows. InclusionAI has not raised outside capital and has no disclosed valuation of its own; it is funded entirely out of Ant Group's internal budget as a strategic research program, the same way Google DeepMind or Meta's FAIR operate inside larger parent companies. No public reporting ties a specific dollar figure to InclusionAI's annual spend, distinguishing it from independently financed labs such as Anthropic or Mistral AI that raise and disclose named funding rounds. The lab does not sell its models directly. Every major release ships as open weights under the MIT license, downloadable from Hugging Face and GitHub at no cost, which functions as a talent-recruitment and ecosystem-building strategy rather than a revenue line of its own. The commercial payoff for Ant Group is indirect: Ling and Ring-derived models feed Ant's own financial-services products, including the Ling-3.0-flash-Fin model built specifically for investment research and spreadsheet-heavy financial workflows, while the open releases build developer goodwill and recruiting pull against better-funded Western labs. InclusionAI's Hugging Face organization page lists roughly 60 named researchers and engineers as of 2026, though that figure likely understates the lab's true headcount since it only reflects publicly credited contributors. Leadership sits with Zhengyu He at the executive level and Richard Bian on product and growth; beyond those two, InclusionAI does not publish an executive roster or board, consistent with its status as an internal division rather than a standalone company. InclusionAI describes its own mission, in the words on its Hugging Face and GitHub pages, as "an earnest yet small wish to bring small and usable intelligence to the world," emphasizing efficient, deployable models over sheer scale for its own sake. Beyond language models, the lab has started publishing early work on Embodied AI, aiming to connect its digital models to physical-world robotics tasks, and treats every major release as a public technical report with benchmark data rather than a marketing-only announcement. InclusionAI competes most directly with China's other open-weight labs. Against DeepSeek, its Ring reasoning line trades blows on agentic and math reasoning benchmarks while training at a reported 20% lower compute cost by using domestic Huawei Ascend and Alibaba-made chips instead of Nvidia hardware. Against Alibaba's own Qwen team, Ling competes on general-purpose open mixture-of-experts models, and against Moonshot AI's Kimi line, InclusionAI's Ring models compete on long-context agentic reasoning. InclusionAI's edge is Ant Group's balance sheet and its access to Alipay-scale financial data, which none of those rivals can draw on directly; its gap is the absence of the independent governance and safety disclosure that Western frontier labs publish as standard. Training on domestic Huawei Ascend and Alibaba-fabricated chips instead of export-controlled Nvidia GPUs is a deliberate response to US export controls on advanced AI chips to China, and Ant Group has said publicly that the substitution cut training costs by around 20% while matching Nvidia H800-class results. InclusionAI has not disclosed an EU AI Act compliance posture or a China-specific AI regulatory filing, and as a China-based, Ant Group-funded lab distributing MIT-licensed weights globally, it sits outside the compliance frameworks, such as SOC 2, HIPAA and GDPR data-processing agreements, that Western enterprise-facing AI vendors publish.
Mission
An earnest yet small wish to bring small and usable intelligence to the world.
Products
- Ling-2.6-1T (Flagship open-weight non-reasoning MoE LLM): https://huggingface.co/inclusionAI/Ling-2.6-1T
- Ring-2.6-1T (Open-weight agentic reasoning MoE LLM): https://huggingface.co/inclusionAI/collections
- Ming-Flash-Omni-2.0 (Open-weight omni multimodal model): https://github.com/inclusionAI/Ming
- Ling-3.0-flash-Fin (Finance-domain fine-tuned LLM): https://huggingface.co/inclusionAI/Ling-3.0-flash-Fin
- LLaDA 2.1 (Open-weight diffusion language model): https://huggingface.co/inclusionAI/collections
InclusionAI Models on HokAI
Links
Frequently Asked Questions
How is InclusionAI funded, since it doesn't raise venture rounds like most AI labs?
InclusionAI is not a separately funded company; it operates as an internal research division of Ant Group, the Chinese fintech affiliate behind Alipay, and draws its budget directly from Ant Group rather than from outside investors. No independent valuation or funding round has been disclosed for the lab itself. That distinguishes it from venture-backed rivals such as Mistral AI or Anthropic, which raise and disclose named funding rounds.
What AI models does InclusionAI make?
InclusionAI publishes four open-weight model families under the MIT license: Ling for efficient general-purpose text, Ring for agentic reasoning, Ming for multimodal any-to-any generation, and LLaDA for diffusion-based text. Its lineup spans trillion-parameter mixture-of-experts flagships down to smaller, domain-tuned releases like the finance-focused Ling-3.0-flash-Fin.
Does InclusionAI publish compliance certifications like SOC 2 or GDPR?
No. InclusionAI has not published SOC 2, ISO 27001, HIPAA or GDPR certifications, and it does not operate a customer-facing trust center. As an open-weight research lab that distributes free MIT-licensed model files rather than a hosted, contracted service, it falls outside the compliance frameworks that enterprise AI vendors typically publish; any compliance obligations sit with Ant Group as the parent company.
Who competes with InclusionAI in open-weight Chinese AI models?
InclusionAI's closest rivals are DeepSeek, Alibaba's Qwen team and Moonshot AI's Kimi line. Its Ring models trade blows with DeepSeek on agentic and math reasoning benchmarks, while Ling competes with Qwen on general-purpose open MoE models. InclusionAI's advantage is Ant Group's balance sheet and access to Alipay-scale financial data; its gap is the lack of published safety and governance disclosure that Western labs provide.
How do I start using InclusionAI's models?
The fastest entry point is Hugging Face, where InclusionAI publishes model weights, cards and benchmark data for each release under its inclusionAI organization page. Code, training recipes and inference examples are mirrored on GitHub, and third-party providers such as OpenRouter and Fireworks AI host inference endpoints for specific models, including Ling-3.0-flash-Fin, without requiring a direct InclusionAI account.